Entangled Health with Thomas Ehmer | Quantum Biology, Consciousness & the Future of Medicine

UL la meme.
Welcome to Entangled Health,

where we explore the
intersections of quantum

science, AI, biotechnology, and
human well-being.

My name is Thomas, and I'm
convinced that health innovation

happens at the entangled
boundaries where cutting edge

physics meets biology, where
consciousness research

intersects with technology, and
where visionary thinking

transforms into practical
solutions.

We are bringing together
pioneers, researchers, and

innovators who are reshaping how
we understand and experience

health.
What if our computers weren't

made of silicon, but of living
cells?

What if the music we listen to
wasn't just composed by a human,

but Co created in a duet with a
single celled Organism?

My guest today is a pioneer who
lives and breathe these

questions.
Professor Eduardo Miranda is a

composer and the director of the
Interdisciplinary Center for

Computer Music Research at the
University of Plymouth.

He's a globally recognized
leader in the fields of

biocomputing and quantum music.
His work doesn't just push the

boundaries of art, it forces us
to reconsider the very nature of

intelligence, creativity, and
life itself.

Today, we'll journey from the
concert hall to the Petri dish,

exploring Hop office and Miranda
harnesses, slime molds, and

quantum computers to create art.
And we'll ask a provocative

question.
Could these living symphonies

hold the key to future of
Entangled adaptive and truly

personalized healthcare?
A quick disclaimer, all chats at

Entangled Health are a Co
creation between the guest and

myself and not an official
statement from our industries.

We are here in a personal
capacity so everything we share

is just our own.
Take not the official view of

any company nor of my employer
or any other organization today.

It's a pleasure and a really
honour to have you.

So, hello, Eduardo.
Hi Thomas, it's a pleasure to be

here.
Nice to see you again.

Yeah, really nice to see you and
our frequent listeners know that

I'm always keen to asking my
guest as a first question what

direction of your action was
when I reached out to talk about

entangled health.
So your work is not about only a

creativity but in principle
health and everyone even awarded

by the Queen.
Can you just tell our listeners

a bit what you, your
interpretation or your action

was?
My interpretation of entangled

health, I'm not sure I would say
something that looks maybe to

approach the body and mind and
overall well-being from the

perspective of maybe quantum
mechanics and quantum physics

was entangled kind of suggests
quantum computing and and things

like that.
So I would say that it's

focusing on understanding how
fundamental energies that make

up the universe, like sounds,
light, magnetic fields and so

on, may affect the human body
and mind.

Really good.
So at least because you, you

bring up sound and, and, and of
course you bring up.

Sound, yeah.
I mean, it's when I heard this

term first time I've I was not
quite sure, you know what, what

to make out of it.
But it's a it's a nice,

intriguing term that makes I
think brings ideas that are not

so, so clear cut.
Yeah.

And I think that that's exactly
the ambition of the podcast, to

really bring people together who
should know of each other and

but are in different domains.
And then you just don't have the

overlap.
And then, yeah.

I, I like that.
And I heard some of your

previous editions of the podcast
and yeah, I, I, I really had a

good time listening to them.
So thanks.

I'm very pleased to be here.
Thanks.

So the, but before we start
going into what we got to know

each other better is quantum or
slime outs.

I think it's always good to
start to know a bit the person

who makes us who we are, where
we come from, etcetera.

And I think you have quite a
long history in composing but

also engineering.
So wherever you want to start,

if you look back at childhood.
Can you?

Yeah, I mean, I've, I've been
always a musician, so to speak.

I've I I used to say that my
first musical instrument was the

amateur radio of my dad.
He used to be a Kinamoto radio

and I used to play with the dial
to make a, you know, to to be

out of tune sort of sounds and
all Gladys space age kind of

sound and stuff.
So this now, since very early

on, sounds was my thing.
And then then I started studying

a piano as as we do when we were
a kid.

I started playing the piano 6-7
years old.

But you know, it's a, it's a
common thing.

I mean, when you reach the, the
age to, to go to college, then

you have to make decisions in
life what, what you are going to

study.
And, you know, it was clearly

that a music career was not
particularly, you know,

something.
Yeah, No, it's interesting.

But I would not have been.
No, I, I didn't think I would be

a performer, A pianist or a
violinist to, to play with an

orchestra and so on, because I,
I was not that good.

No, I like to play.
So I decided to do computer

science.
Now that was the mid of 1980s,

and I went to college, did
computer science, you know,

programmed computers with
punched cards.

You know, I don't know if you
remember those.

You know, you submit a stack of
punch cards to the computer and

then next days you get the
results and things like that.

So it's more or less what we do
with quantum computers.

Today.
But anyway, so I did that and

then I graduated and I got a job
as a system analyst in a, in a

company in Rio de Janeiro.
And I was developing systems to

for automation of supermarkets,
you know, kind of a barcode scan

barcodes, then get prices and
stock lists and.

They also make music, by the
way.

Yeah.
But yeah, but I mean, after,

after a year or so, I, I've, I
got a bit frustrated with that

kind of thing in a way.
I thought, this is not for me

really.
And, you know, I decided to

change course and I, I went back
to university to do music.

And then I've, I was doing
conducting, I was studying to be

a conductor.
And one day I was in the library

of the the music school and I
found a book which when I opened

the book, no, I could not
understand much of it because it

was in French and but I saw Venn
diagrams, logic equations and so

on.
I thought, wow, this is not

music.
This is this is, these are

things that I understand.
And then, you know, it was

written by a composer called
Yanis Xianakis, who was, you

know, at the time a contemporary
music composer.

And, and he was talking about
music with terminology that I

knew.
And then I thought, wow, this is

great.
So this is what I'm going to do.

I'm going to do mathematical
music.

I'm going to program computers
to make music, you know, because

I had the knowledge to bring
that, that theoretical framework

in into practical, you know,
practical musical compositions

and so on.
So I, I decided then to, to stop

the music course and, and, you
know, and dedicated myself to,

to using computers to, to, to
make music with.

And there was also a very
practical reason for doing that

because the music I was
interested in composing, it was

very difficult to get performers
to play, you know, So what, what

I realized that, well, maybe if
I, if I start making electronic

music, I can make my own sounds
and I can program machines to

play my stuff.
I, I don't need to be begging

orchestras and musicians to play
what I was writing.

So that was a convenient move, I
say.

But talking to, you know, to a
friend of a friend of a friend

of a friend who said, oh, well,
well, I know this professor in

the, in the, in the computing
school that was in Brazil that

he is, he's very interested in
formal logics and he likes

music.
So maybe you should talk to him.

Then I went to talk to him and
said, look, I'm a, I'm a

composer and I've, I have a
computer science background and

I would like to, to, you know,
to, to collaborate, you know,

to, to see if we can get some
project together and so on.

And he convinced me to enroll in
a, in a master's in AI.

So I, I started doing a master's
in AI and then it, it turns up

that I got a scholarship to go
abroad and I went to, to the UK

to do a master's degree in
electronic music.

And then I enrolled at the
University of Edinburgh AI

department to do, to, to do a
PhD in music and AI.

So I I think I was one of the
first person who did this kind

of PhD.
I graduated in 19/19/1990.

Early days.
With a APHD in using AI to make

sounds.
This is interesting.

And so, yeah, I mean, I've been
doing AI and in music before AI

became.
The hype.

Became the hype.
So at the time we were working

with a symbolic AI, you know,
the neural networks were not

even, they were not even known
in the yet, you know, everything

you know was rule based,
symbolically coded and so on,

which I think is still the way
to go.

No, but to.
To mix, yeah, we can maybe later

speculate.
Yeah, but that's that's my

opinion.
But yeah, so that that's it,

Thomas.
That's how I started working

with with computers and and
music.

So I don't see a distinction
between art and science and

music and science.
So for me they are the same

thing.
And this is really an

interesting perspective.
We currently need more to have

bit more the insight that
science is creation, music is

creation, and many of the very
intelligent scientists have been

musicians and Einstein for
example.

Yeah, I mean, I, I, I, I think
the distinction.

Of course there are
methodologies.

I mean, if you are, you know, if
you are working with, you know,

make have to come up with a
vaccine, you know, there are

methodology to do that.
You know, you have to be

thorough, you have to be, you
know, you have to have evidence

of things and so on.
But there are other

applications, there are other
situations where our more

artistic approach is is
perfectly.

Yeah, sound sound therapy.
You know, and, and I think there

are shades in, in, in, in
between and.

And this is where I am.
And I, I can clearly see in my

research, for example, when I
needed to be, you know, when I

need some results to be proven.
So then I go for it and and I

try to prove the results in a in
a more objective way.

But that does not mean that my,
my music needs to be objective.

I can be very subjective based
on the objective results that

you can get from an algorithm
or, or something like that.

And there were cases, Thomas,
that where I, I was, you know,

I, I was kind of stuck on some
experiment or, or some, you

know, especially when I worked
with brain computer interfaces

where I, I, you know, if I did
not have the flexible approach

that I, I do sometimes, you
know, have when I'm doing

science, I would probably not
have done the, the things that I

have that I have accomplished.
I think as you just mentioned

this PCI is a brain computer
interfaces.

So you have, if I'm not wrong,
it's called paramusical

ensemble.
That's right.

Can can you tell me more?
Because this is really

fantastic.
So I, I just saw that all I I

recall that you enable disabled
not mentally, but but

physically.
This is the person to do to do

music again by reading their
brain waves.

So this is.
Yeah, this is again, it's, it's

all by chance that I, I came to
do that because, you know, being

an AI person, AI is to do with
modelling intelligence and

modelling processes, cognitive
processes and so on.

And you know, I, I have always
been very curious about, about

the brain, you know, how the
brain works and so on.

So I've been studying, you know,
neurobiology and, and

neuroscience to be able to
model, you know, how how the

brain works and cognitive
processes work and so on.

And I've met a musical therapist
at a conference, in a

neuroscience conference, Wendy
McGee, and she was at the time

working at the hospital Royal
Hospital for Neuro Disability in

London and chatting to her, you
know, well, I'm a musician.

I I've very interested in, in
understanding the brain and so

on.
Then she said, well, yeah, I'm a

a musician there as well, but I
work with severely motor

impaired patients and I try to
use music to, you know, as a

palliative maybe care component
to, to make them happy or, or

more responsive or something.
But then she mentioned to me

that one of the problems that
she identifies that music is

powerful, but but listening is
not enough.

You know, you have to make it.
You have to be engaged in making

music so that not to activate
your your brain is in specific

ways.
And then I had this kind of, you

know, the brain waves, so to
speak now.

Well, maybe we could, we could
try to develop some form of

interface where we could read
signals from, from the brain in

a way around another and using
the signals to make music,

perhaps as a musical instrument.
And this is then how I started

looking to this.
I I knew at the time that the

electroencephalogram could be
read with sensors.

Hospitals use still, you know,
use very much it's a it's a very

well established technique
exactly to read the, the

electrical behaviour of the
brain.

And I, I, I managed to convince
my department here to buy a, an

EG equipment.
I hacked it because at the

moment it was at that moment,
the EG equipment was just to

plot the, the, the graphs.
But I actually, I wanted to get

the signal and work with the
signal.

So then I, I began to study the
signal.

I began to realize that there
were signals that correspond to

different cognitive activities.
The simple one for example is

when you are focusing attention
on something you you produce a

lot of the so-called alpha
waves.

If you are distracted, if you
are no not focusing on anything,

you tend to produce waves that
are are noisier.

You know, the so-called beta
waves and, and I began to use

machine learning to be able to
distinguish between these

different kinds of electrical
brain waves that, you know, try

to classify them according to
the specific cognitive tasks

that people were doing.
Then I realized that it is not

as easy as as one may think it
is.

You know that we are very
different from each other,

Thomas, Our, our brains is, is
like our fingerprint, right?

We are very different and trying
to find common ground between

people's brain is, is finished.
It is really difficult.

So then I, I kind of began to
look into other other

techniques.
For example, what if I could

stimulate, you know, give a
stimulate to, to people to, to

stimulate the brain somehow, and
then use that for, for making

people to train themselves to
produce a specific brain waves.

So after many, many years of
experiments with PhD students,

research assistants and so on,
it took about 8-9 years to to

get into a a place where I
could.

Train people to produce specific
kinds of waves.

We developed a method for doing
that, and then we developed the

system where depending on
specific signals that patients

in this case could produce, I
could use them as switches to

switch things on and off.
It.

Could be a media controller,
could be a switch for, for, for

anything really.
In a way, in my case, great

musical instruments.
And yeah, and, and then we also

developed a more precise way to
to use the the brain signals to

control things, which is to
focus on the activity of the

visual cortex.
So by putting electrodes at the

back of the skull, we're we're
able to to tap into signals that

corresponded to the visual, the
visual stimuli.

And interestingly, we worked
with the idea of is stimulating

the brain with different,
different frequencies of light.

In this case, it's, it's very
simple.

If you have icons flashing at
different frequencies on a

computer screen or, or a, or a
light bulb or whatever, you can

detect at the, at the resonant
signal exactly.

You can detect in the EEG of the
visual cortex the frequency of

that flashing, of that flashing
stimulate.

So for example, if you have a
flashing light flashing at over

5 Hertz speed, another one 7
Hertz, another one of 13 Hertz,

we could detect those components
very clearly in the EG and then

we could use that as ways for
people to make choices by

looking at things.
High and then you say OK, if if

I play the five Hertz, it's AC
and if I play the 13 Hertz it's

whatever the.
That's it.

But then there is another effect
which is very interesting, which

is the more intent you look at
that particular stimulate, the

higher is the amplitude of that
signal in the brain wave.

So then we, we, we were able to
use that not as, not only as a

switch, but as a potentiometer.
We could switch things on and

make it stronger in, in, in, in
light, you know, in, in, you

know, higher and lower, which
means that we could have.

Now imagine you have a mixing
desk with the faders.

Yeah.
And then you.

You could get the faders up and
down by looking at them.

So I think if if if we would
have the translator to cut in

some audio audio files, just an
example for the listeners to

have.
Maybe we can check if we have.

Some, yeah, yeah.
What can I can give you some

examples, But what what we did
then we developed all sorts of

interfaces for for people to
play with.

So we had, for example, little
phrases on the computer screen

that then they could select the
little phrases play with a MIDI

controller or play and a
synthesizer.

But the, the idea of the
Paramusical ensemble went a

little beyond that.
We developed, I developed a

composition that's a kind of a
rubric cube, right?

You can see, you can select,
it's a kind of a complex domino

where you put, you know, you put
a card on the table, then there

are many options to follow that.
Now you put another one, then

there are many options to follow
that.

So my my composition was a game
where you could select a bar of

music and then the system would
provide you many others that

could follow that one.
So this means, for example, that

a person could select with the
with the brain signal, one bar

of music and then the system
would give many options to

select others.
And each option was flashing at

a particular frequency that you
could look at them and and

select them.
But what we did, instead of

using a synthesizer, we sent the
options to A to a computer score

for a real musician to play.
So we had a string quartet and

we had a quartet of, of motor
disabled impaired people and

they were sending those segments
to this for the three quartet to

play.
So there was a that's why why we

called Para Musical Ensemble,
because it was a quartet of

brains composing the music for a
a string quartet.

In principle similar to what we
did or what what you showed in

the Ligeti Museum back then in
Hamburg, where in but instead of

having it generated by brains to
perform with violence, the

violence performed for the
quantum computer looping it,

maybe we can talk.
About yeah, that was AI mean the

approach is more or less the
same.

You know the I think the key
thing here, from an artistic

point of view, you need to make
the music to make sense.

Yes, which is not so easy.
Yeah, exactly.

So I spent a lot of time trying
to to make this highly

combinatorial piece that no,
that would not matter what

combinations you got, it would
always sound OK, sometimes more

OK than other times.
And this also enabled the, the

performers to make choices.
You know, I, I could see, for

example, them playing with each
other right now.

I'm going to put this session
here.

Now that's really difficult to
follow.

And then and then the other one
would find you.

You play, so it's too
complicated.

Yeah.
And, and it was interesting

because at the end of the day,
what the, the medical staff at

the hospital told me is that,
you know, this was brilliant

because those patients, they,
you know, technology is nothing

new to them because they are,
they have to use technology to

communicate.
And but this technology that are

developing that are developed
for those always a one to one

thing, it's a one person in
front of a computer and a

computer giving feedback for
that person.

But what we created here was a
way where 4 people were able to

communicate between themselves
and that is.

Yeah, that's the social
component.

Exactly.
They were playing with each

other, even though they were not
able to talk, they were not able

to make gestures and so on.
But they were paralyzed from the

neck down.
They only had very little eye

movement or mouth movement,
right?

And but they were talking.
They were communicating emotions

to themselves.
Which is themselves for, for a

real, for, let's say for a band
musician, the worst thing that

can happen is if you can't
interact with with your

bandmates because you, I mean,
it's just communication.

Music is just.
Correct.

Yeah.
And you and maybe this is

relates to your entanglement
thing, right.

Yes, I think those, those, those
for those four, no, it's a

locked in syndrome.
That's what it's called.

Those four people, they were
communicating between

themselves, right?
And in a way that was not

possible before.
And I was always thinking if one

could even, I mean, if you
imagine music or you have some

thoughts and you're really
musician, so you have your

inner, inner whatever things as
a composer.

And an ideal way would of course
be instead of transforming this

to motor movement to sound
movement, back to movement in

your inner cells in the ear and
then the pulsing and then

generating thoughts in your in
your head.

Yeah, I mean.
The brain to brain

communication.
Yes, this, this is, this is a

difficult thing.
I I confess that I I was looking

into this.
I will OK.

And but the, no, it's easy to
read the brain, but it's not

easy to put things inside our
brain.

I mean that, that, you know,
that there were many attempts,

even actually last century,
there were many attempts doing

this, that there were very, very
weird books I found of people

trying to, you know, to
stimulate the brain.

People can do that, you know, to
stimulate the brain to for a

specific treatment, but to put
ideas inside our brain that

that's impossible, that doesn't
work.

And I think this this brings me
to I'm I'm not trained in how

far you buy into the the the
thought.

But there there is the idea that
we have a new sphere or the

ideas are outside of our head.
Yeah.

Our brains are just tuning in
and I'm selecting this the

channel.
And therefore, I think it's of

course quite difficult to put
things in the brain because if

they're outside of the brain by
definition.

Exactly.
Yeah, it's rather better to put

the read the signal and put it
in the ather however you might

want to call it.
Coming.

Yeah, I mean in many ways, you
know, a brain computer

interface, if you have wireless
communication, if you have a

very small electrodes in very
good amplifiers.

Now you could relate to some
sort of telepathy.

You know, even though if I send
my, my telepathic thoughts to,

you know, it would be, it would
be almost impossible for you to

think, you know, to, to know
what I'm thinking.

But you would get some signal of
some sort.

And, and that signal may do
something in your body.

But I think this is not, you
know, it's, it's not

communication in the sense of
how we understand communication

today.
It is an, it's an entanglement.

Let's say it's a.
It's a connection that may

happen, but.
Yeah, it's not, it's not really

the the own, but I, I think the
as a band musician, sometimes

you have these.
So you jam together with a band

and, and, and you look at each
other and in one moment in time

you feel a bit more than
yourself.

That's where you're you're just
performing as an ensemble.

And then everybody just like a
flock of birds, switches in a in

a unseen spontaneous reaction.
But everybody's going these.

Things happen.
These things do happen and, and

it is in music performance.
This is something that people

study a lot, you know, in music
psychology and so on.

And you know, how to listen to
each other and, and, and just,

you know, being the flow of
things, you know, and this

happened.
It's not only music in anything,

you know, and these things can
happen, but it's not well

understood and it is very
difficult to figure out what's

going on.
Maybe these kinds of

technologies that we are
developing now can help in the

future to understand what's
going on.

But there, there is some sort of
communication going on that is

implicit, maybe by chance or
maybe entrainment, maybe, maybe

when we entrain to each other.
And, and if you, if you are with

a band of jazz musicians
improvising and you really get

on with it, you know, you know
each other, the magic happens.

But but the magic does not
happen when you don't entrain

when you don't.
That's right.

When you don't connect.
So there is something that needs

to to be done, needs to happen,
but we we cannot force it.

We do not know how to do it so.
I'm, I'm, I'm keen to just just

go a bit deeper into the sensing
part because we just don't know.

And I'm always keen to explore
quantum sensors when they, they

produce classical data, of
course, but we, we're just

digesting the potential of very
weak magnetic sensors with heavy

sensors or what have you.
Do you think this might be

helping also the creative part
or even then going more into

into this brain computer
interfaces for whatever

actually?
Or definitely I, I think sensing

is the key here.
I'm very interested in quantum

sensing.
But you know, because the, the

brain, I mean living Organism
produce signals, right?

That, that, that's, that, that's
it.

So we can, we can detect the
signals.

They are very faint and the
technology that we have today

are not so good.
I mean, we, we have fMRI

machines which are, you know,
quantum if you think about it,

but it it's very dependent on
modelling.

So those machines are actually
very sophisticated predictors of

what they expect to see.
So, and it takes a lot of time,

you know, it takes four or five
seconds for a full scan to

happen in full brain scan to
happen.

So that's why MRI machines are
not good for brain computer

interfaces.
First, because they are too big,

you cannot take them around, but
the time of resolution is very

poor.
It should be very long lasting

musician.
Yeah, but in the electrical

signals are there, but you know,
unless you open the your skull

and put electrodes inside inside
the skull then the signal.

Trying to do this, but I'm not
so sure it's a good idea.

The signal you get is is very
poor.

Yes, yes, let let maybe just
just switch a bit just because I

I on on Discord, for example,
you're the bio computer, so you

handle this bio computer.
You want to talk a bit on why

this is?
OK, well-being, someone who is

interested in EAI and someone
who has an avid interest in

neuro neuroscience and brain
science and so on.

From very early on, Thomas, I
realized that AI is software

right?
And software needs hardware to

run, and AI today runs on a
piece of hardware that has not

changed very much since the
1950s and 1940s.

No, it's all switches.
It's all digital.

It's all digital.
Yeah.

It's.
One and off 1 and off and and I

always wondered is there any any
other kind of computer out

there?
Can we design any other kind of

computer?
Can we harness other materials

to do computation for us?
And of course, one, one of the

one of the natural progression
here would be to, to build, you

know, to build computers using
real neurons.

I mean, that's what some people
are doing this now.

But I always wanted to do this.
And then over 10 years ago, I

think even perhaps more, I, I
teamed up with some people here

in Bristol, just close to
Plymouth at the University of

West of England.
They were looking into growing

neurons on, on, on Petri dishes,
multi electrode array dishes.

So they were taking neurons from
chicken, from chicken embryos,

growing these Petri dishes, and
then stimulating the neurons and

look at the microscope to see if
they could foster synapses,

natural synapses.
Then I thought, oh, this is

fantastic.
So I want to do this.

So, yeah, I mean, we, we, we, we
began to look into, you know

what, how can we use these
signals to make something with

it?
You know, we, we even developed

a little synthesizer that we
could, we could put, put

electricity into the, into this
chicken brain and, and then we

read the, the, the spikes of the
neurons and use that to, to

control the synthesizer.
So that was, it was pretty cool.

But then, you know, it was clear
that at the time it was very

difficult thing to do.
And so then I came across with,

with another Organism, which was
much simpler, but behaved quite

similarly to, to those neurons,
which is this line mode, you

know, if I saw polycephony.
And then I became hooked to

this, fascinated with this
Organism because you could

foster, you know, the, the, the
shape of the, how it grows.

And, and not only me, but other
people at the time realized that

these organisms could behave as
some sort of pretty much like

electronic components like
resistors, capacitors, and even

a component called meme.
Resource.

So, yeah, so this is how I began
to, to looking to know how can

I, can I develop a computer to
this?

Can I, can I harness these
organisms to, to process data

for me?
And we managed to do a few

things now.
We built logic gates with these

line mode, we could add 2
numbers, we could, we could do

an or gate, an end gate.
And also we built, we built

little circuits, very, very
simple, but nevertheless they

were there.
They were gates.

So we and this was it's analog
stuff.

Of course it's analog.
So it's not pulsing.

So you.
No, no, it's analog gates.

Analog because we call gates
because if it went above a

separate threshold.
Then.

It was one.
If it was below the threshold,

it was 0.
So we could simulate those

things.
But what was interesting is that

we could kind of.
Make this Organism to to

memorize to to memorize the
stimulate that we input to it.

So in this is essentially what a
memory store does.

So we devised at the end of the
day, we devised a circuit that

we could input electrical
signals to it.

Then we could read current and
we use these readings to build

some sort of Markov chain, if
you like, OK, we we could know

it's a Markov chain where the
the transition probabilities

were not numbers, but the
current that we could read from

this Organism.
So then of course as a composer

that in order to make music.
Interesting.

Right.
So what what we did, we

basically, I say we because
there was myself and two PhD

students of mine, but we, we
developed this machine, a

biological machine that could
read notes.

These notes were then
transformed into into voltages

that were input into the system.
And then the system memorized

the sequence of notes as a kind
of a biomarkov chain.

And then we could ask the system
to generate sequences based on

this learning in the form of
current.

And this current was then
transformed into into musical

information.
So that's when I came up with

this idea of the bio Computer
Music concept where I had this

bio Organism that, you know,
that could listen to what I was

playing and could make responses
back to me as if it was a, a

musician improvised, you know,
and it, it works, you know, it's

there.
I still sometimes play around

with it.
Of course it's it's a very

limiting system because for fun
is good, but there is no use for

it because, for example, we we
need to replace the the the

design mode component very, very
frequently.

OK, because even during a
performance it, it, it dies.

Oh, well, yeah.
Because if you are also to say,

yeah, if you are inputting lots
of voltages to the thing.

Now if you you fry it basically
so it you can.

Eat it afterwards, yeah.
So there, there must be a ways

of doing it.
You know better than we did,

like, you know, I was talking
with synthetic biologists, you

know, the, to see if there would
be a way to, to fabricate these

organisms in a way that would be
robust and, and etcetera,

etcetera.
Everything is possible

theoretically, but in practice,
it needs time, it needs money,

you know, and I know if I only
had the the cash that the

quantum computing industry has
at the moment to develop these

things, I would develop.
How do you if the if the AI

bubble bursts?
We have billions and trillions

and quadruple out Googling.
Whatever yeah exactly.

And, and again, and this is what
led me to, to look into quantum

computers basically because I, I
put the bio thing aside for the

moment.
But, and I realise, OK, you

know, in my quest for new kinds
of hardware, it seems 5-7 years

ago, seven years ago, it seems
quite natural to investigate

what these people are doing, you
know, harnessing quantum

mechanics process information,
which is very interesting.

I think that this is this is
what I'm doing at the moment.

You know, because I think there
are, there are different ways

and I mean, we, we know our
friends from moth and and all

the other guys who are doing
quantum using, using in

principle the, the block sphere
right to generate FM

modulations, etcetera.
But I was always wondering if

there is more to it because if
we really go deep and say

entangled.
So in principle, you could say

I'm making music and store it as
quantum information, and every

participant at the concert, he's
observing it with his own

observation, and then eventually
he's hearing something else.

Yeah.
Yeah.

I was just wondering if this is
technically possible or if it

makes sense because it's always
difficult as a musician to use

things to make music because
sometimes it's just noise and

and if you really want to if
you're in the groove.

Yeah, yeah, I, I, I see.
I mean, the, there are many

things that can be done with
quantum mechanics and, and it

took me time to learn all this,
but now I, I think we can, we,

we, we can harness these kinds
of computations to do very

interesting things.
And for example, I'm working on

a piece at the moment where it's
very nice that we have access to

hundreds of cubits now, because
what I'm doing is I'm

representing my musical
composition now.

It basically I'm going back to
that, the cube, the Rubik cube

that I did for the for the Brain
project.

But what I'm doing now is much
more sophisticated.

I have this very complex musical
materials that can be combined

in infinite ways, OK.
And this musical material, now

I'm encoding it as a quantum
circuit to generate a quantum

state.
OK, so the idea is that every

time I run this this this
circuit, it will generate

quantum state.
And when I measure this, if you

give me one version of the music
and if I measure again it give

me another version of the music.
So this means that I have

instead of having a sound file
in my iPhone or my computer,

what I have is a quantum
circuit.

So my music is represented in
terms of a quantum circuit that

generates a quantum state and
every time I listen to it maybe

How will you get a different?
Yes, yes.

This is, by the way, also the
quantum question pool.

So there's this pool and then
it's it's just, it's just

running because it's it's.
Yeah, that's right.

And, and you can imagine the
case, you know, you go to a

concert and, and, and everyone
will listen to 1 version of the

piece.
You know, it's, it's, this is

interesting because it's
different, right?

It's a different concept then.
It's a different concept then.

I have a generative system that
generates a different sequence

every time.
In this case here I have the

whole piece as a quantum state
and when I measure it I get it

instantaneously out.
And one times you get a Grammy

and the otherwise you get boost.
We don't know, we don't know

what you get, but that is what
I'm working at the moment.

So I'm it's very tricky to
design this kind of

representations and to get the,
you know, the, the, the, the

proper quantum circuit
representation and so on.

But yeah, watch this space.
This is I.

Think it's also if I mean, if we
see these days all the Gen.

AI and all these legally or not
legally sourced thing.

And as an artist, I mean, you
run you run Zuno or whatever and

then the music which is produced
by the machine is not always

worse than what you could come
up with yourself.

Yeah.
And I think this is really hard

also for musicians to make a
living these days if you're not

really have one of these super
brands.

And I think that might be really
way, way out that you say I'm

more, it's more live performance
and it's, it's a unique

experience and either you pay
for it or not.

Correct.
I'm not interested in AI to

imitate people or to RIP off
people and no make pastiches by

combining many things, you know,
dogs with four legs and, you

know, cats with three years, you
know, I'm, I'm not interested in

that at all.
What I'm interested is in using

this, harnessing this, this
technology to to come up with

something that's different.
And but the human agency is

important.
You know, I don't think machines

will ever replace a human agency
from the creative process.

No, a machine can create some
cute things.

Yeah, well, nice, blah, blah.
But the the agent, the human

agency is important because art
and music and and art general is

communication between you know,
for from humans to humans.

And I think it it's also coming
back to healing.

So it helps.
I mean, you really see it if

you, if you listen to real
things, people react

differently.
So I have that's right.

I think that you can.
Yeah, it's, yeah.

I mean, it's, it's interesting.
There were so many cases that,

you know, you can get a
computer.

I've done this so many times to
program a computer to to

generate no musics like Mozart
It's easy you can do that the

rules are all there but it's not
the same thing.

You know, it is cute, it is, it
is curious, it is nice and it

occasionally I think it is AI
tools are very nice because they

they enable us to analyze the
piece of music to make sense of

the music.
Although the AI we have today is

completely useless because it
does not tell us how it achieved

what it achieved most.
Of it.

Doesn't work.
No, it's not.

It does not explain anything.
You know it.

It does not tell you what bits
and bobs it ripped from here and

there to make something.
And, and this is not useful, I

think for the creator.
It's good at creating, but it's

not good at supporting creators,
you know, And this is what we

need.
I'm, I'm just thinking you

because I, I you, you did
compose this opera Lampedusa.

Oh yes.
And when you and I I I I recall

you said you even developed an
own language.

Yes.
Tell me more.

So I need.
I need.

To OK, I didn't I did not
develop the own my language.

This is, I have always been
fascinated by, by language and

when I worked for, for Sony a
few years ago, I worked in the

linguistic computational
linguistic group.

And we are looking at modelling
the origins of the language,

how, how language evolve, you
know, and how, what, what makes

people you know, cultures and so
on develop different languages

and so on.
And completely by chance, I was

giving a talk at a big
conference in in California a

few years ago, and I met David
Peterson.

David Peterson is a language
creator.

It's called Kong Langer.
This profession, I did not know

it.
It's a language creator.

He creates languages for for
Hollywood films, right?

Or.
Whatever the the Dothraki

language for the Game of
Thrones, for example, he created

it.
But when I watched these films,

I thought, oh, this is all
gibberish, you know?

But it's not actually.
He makes the grammars.

He, he, he makes everything.
And there are, there are people,

there are clubs of people that,
that learn this artificial

language to talk about in
themselves is completely weird,

you know, And I, I was talking
to this guy and he said, look,

I, I have an idea to compose an
opera, but I wanted to be in an

artificial language.
Can you make the language for

me?
He said, of course, this is what

I do.
So then I, I told him, OK, he

asked me, what do you want this
opera to be about?

Well, this opera will be about
the birth of the world, the

birth of the universe.
Because I, I'm, I'm using some

data from CERN particle Collider
where this particles model how

the universe originated and so
on.

So I want an opera about this.
And then the discussion was,

yeah, OK, but we need to, to
create a word for it.

We, we need to create some sort
of context.

And he suggested, OK, let's
create a, a language called vov.

Vov involve means love rah Rahul
Giray call Lai UL mem UL mem Lai

tahu kihung git la, git Lu git
la.

And you created this language
and and the language will evolve

itself during the during the
opera.

So there will be a soul.
A few a few movements of the

opera, and every movement is
about an era of this language.

I think, OK, that's really
great.

But then what else?
Where where this opera will take

place?
Then we decided, OK, if you take

place somewhere imaginary like
for example in the islands where

The Tempest Shakespeare Tempest
plays.

But before before Miranda and
Prospero arrived in the island,

right.
If you know The Tempest play.

So before there were just the
Caliban and Cicorax and in this

island.
So we we imagine a pre

Shakespearean story where in
this island called Lampedusa

because many Shakespeare experts
propose that the the opera that

the the play that Shakespeare
wrote Tempest took place in in

this island Lampedusa.
So we measure, OK, let's create

a language for this island
before before the Shakespearean

play and that's where the story
takes place.

But the whole music was
generated with data that I got

from particle collision models
from concern.

So the the music is generated by
particle collisions, but it is

sung in a language that was pre
that predates history.

I think we also need to insert
some song by.

It was completely weird and we,
we got the BBC singers to, to,

to sing the opera.
I was so amazed how these

singers can can sing, you know
because they are able to sing in

Germany, French, modern language
even not understanding the

language they they sing.
The most properly.

And then these singers were, you
know, practicing this language

that does not exist.
And David, was there no poaching

them?
No, you, you say this word like

it is blah, blah, blah.
And, and they, you know, they

learned all of it.
And now they they joke in the

language.
It wasn't wonderful, wonderful

experience for me.
Quite hard work to make an

opera, but some members of the
audience were a bit upset.

They could not understand
anything.

But but you know, we put the
captions in there, in there, the

translation.
And I was just thinking the, the

Lampedusa not also deal with the
topic of migration and all the

social, social company.
Because I think the, the, the

direction I want to go is can
art or music, even if it's

unsung based on Collider data
and in a language nobody

understands, have a deep
emotional healing aspect.

Because you mentioned the
direction of the of the

audience.
I'm really keen to learn on

that.
That's exactly what I wanted to

convey in this opera, the
meaning, you know, instead of

relying on, on the words and the
lyrics and the meaning of the

lyrics, I wanted the meaning to
come from the prosody, from the

way that things were sung from,
from the audio, from the aural

experience, from watching what
they were seeing.

So I, I wanted to convey the
music in purely subjective ways.

And, and, and there was of
course, the acting, there was a

dancer that was dancing.
So one of the the actors, the

characters in the opera was a
dancer and she was dancing the

lyric instead of singing the
lyric, right?

So yeah, to answer our question,
I think music has this way of

communicating things that we
cannot describe with words.

And and I I wanted to explore
this in this opera to the

extreme, you know, to come up
with were a libretto in a

language that was completely
invented and but still be able

to tell a story.
Although they David was very

clever about, you know, about
the meaning, the translation and

so on.
All the things could be

translated into English no
problem.

But nevertheless, the, the, the
performance took place in that

particular language and it, you
know, I wanted to transport the

audience to that world that was
completely.

Maybe it's another word, maybe
it's a multiverse.

Maybe we live in a multiverse,
you know?

You know, maybe that's a word
that could be possible.

We're currently exploring vocal
biomarkers, so the sound and

then the musicology of the voice
and they it's even a clinically

validated marker for people with
their suicidal tendencies or in

depression or whatever.
Right, right.

And I'm speculating that even
the correlations between the

frequencies play a role, but
it's super, super speculative.

I would not even know how to,
how to measure this or how to

analyse the, the vocoder effect
or whatever in in among these

frequencies.
And I'm just thinking that

eventually the the, the, the,
the aspect that you had by going

completely out of text is, is
helping very much in

transporting the deeper the
message because you cannot focus

on the content because there's
no content you understand and

you have to listen to the
aspect.

And I'm just keen to did the
audience or did you, if you

watched the audience while the
piece was performed, did you see

some clusters that behave the
same or was everybody reacting

the same depending or I'm just,
I'm just out of curious.

Yeah, I, I mean, the audience
was an audience watching, you

know, if I go to an opera in, in
sung in German, you know, I

wouldn't understand anything,
but I would appreciate the

spectacle, you know, and I think
people were appreciating the,

the music per SE, what they were
seeing and so on.

But there is one thing that is
important.

I think even with an invented
language, even if we invent

music to be sung in different
ways, we, we have to follow the

Physiology of the human voice
because there are things that we

cannot make with our, our, you
know, there are, there are

syllables that we, that are not
natural and, and we are lazy

brains are lazy.
So we favored the most easier

pathway to, to produce our
words, to sing and so on.

And, and I know that David was
very clever about this because

all the syllables, all the words
that he made, he produced

themselves and these words
himself first to see if it fit

the voice.
If it doesn't, then forget about

it.
Now there there is a reason for

people having the language that
we have and not something else

like the artificial one we made,
right?

And these reasons are
physiological, cognitive.

There is also the, the, what,
what they call the sociological

aspects where we evolve these
things together.

We favour that things that we
hear naturally that we can

produce more naturally.
And, and I think this is

probably connected to what you
are saying about the healing and

so on.
You know this, when we hear

things that make sense in
physiological terms, in the

biology of of things, then we,
we will naturally be attractive

to, to those, to those sounds,
right.

And then this is how harmony
music evolves.

You know, at least in the
Western world, there are some

tonalities that we find more
pleasing than others.

Yes, and this is because of the
languages that we speak.

Yeah, and I think also the
musical sophistication you are,

because if you are a
professional jazz musician, you

might enjoy the 1113 whatever,
of course.

And if you're not, then it's
just maybe a bit complicated

just to listen to it.
Exactly.

You evolve these things.
You, there are some things that

come naturally, but there are
other things that, you know,

some things are biologically, we
are biologically wired for, but

there are other things that are
culturally driven and, and this

is when things become
interesting, I think, you know,

musical tastes and so on.
We are able to distinguish

things that some other people
may not know as musicians and

and other cultures may hear
things in language that we

don't.
And if I try to speak a language

that I never heard before, it
would be completely crazy and

difficult.
I would not hear these things,

you know.
Yeah.

I remember when I was learning
French, you know, living in

Paris, there were sounds that I
could not hear and, and

different vowels that I could
not make, you know, because it

was too late for me for my brain
to adapt, you know, I was

already, I know my plasticity
was already done.

It's just like if you have tried
the the American speak German

with AU and U so it's.
Always.

It's amazing.
Kind of.

And and on the other hand, there
are things you just as a typical

German, you cannot do.
That's it, That's it.

That's that's, that's the the
plasticity.

Thing yeah, that's the the the
beauty of being diverse.

So I'm just I'm just looking at
things like and I want to jump

over soon to the to the quantum
selected questions.

But before we do that, you are a
professor, which means you're

also a mentor or or I mean, and
I guess you have now a lot of

experience based on successful
things and how you deal with

failures and how to keep up and
develop a resilience and how to

always move forward, etcetera.
Could you tell our younger

listeners or I mean everyone,
the the value or or a bit of a

mentor, a mentorship advice how
you.

Oh, it is.
I mean, I think I have formed

almost 40 doctors in my career
so far.

And everyone is different, you
know, and I think to be a mentor

is hard because you need to, you
need to understand the person,

you need to understand the, the
ambition, the, the limitations

and so on.
It is, it's a hard work, but

it's very rewarding to see, you
know, my students out there in

the world doing fantastic
things, you know, some of them

are, are much richer than I am
which.

Is a good thing.
Which is a good thing, you know,

and, and, and they are doing so
well.

But building resilience is, is
an important, is an important

skill, especially being an
academic and trying to innovate,

trying to come up with new ideas
and so on.

New ideas are not very well
received most of the times,

especially in the academic
contexts.

No, academic contexts are are
very close, you know.

Which is strange.
Which is strange, but it is very

close and and to break the
barriers of an academic field,

it needs a lot of work.
And I'm lucky enough that I can

speak, I can speak to different
people.

I can't speak to musician, I
can't speak to neuroscientists

and I can't speak to computer
scientists, you know, easily.

But some of my colleagues
cannot.

And, and that that is difficult,
you know, to, to foster

understanding, to foster
communication between fields.

And I think it is so important
in in building resilience is

exactly that, you know, is not
to be put out because you have

been turned down for something
is mostly because of lack of

good communication.
You know, even when when you

write a paper you get rejected
so many times, it's because it's

not communicated well.
Or if you do, if you don't quote

the the review.
Yeah, I mean the reviewers most

of the time, most of the time I
would say not always are willing

to to give a good.
Feedback.

And to accept what you are doing
right, with exception of grant

reviewers, because because grant
reviewers have other agendas, I

think they wanted the money for
themselves.

But you know, in papers and so
on, I think it's usually a good

thing to be criticized and so
on.

And to build this, what you just
mentioned is, is resilience.

And it's important to believe in
yourself, I think as well to

believe in what you are doing.
You know, sometimes I find

myself thinking, oh, this is
completely rubbish.

Why?
Why I'm doing this.

You know, maybe this is going to
go nowhere.

But then next morning you wake
up and say, oh, maybe I'll do

that.
Maybe I'll continue it.

And.
And.

This happens all the time and I
think.

But if you believe in what you
were doing and and go for it and

be your objective and try to do
the best you can, then I think

you can succeed.
But you know you, you are a

scientist as well as I was.
In my past time I was if.

You know how these things work.
You know, sometimes.

I'm also a mentor and it it's
always really as as you

mentioned, it's really per
person.

It's fantastic to.
Yeah, it it is.

It is great.
It is great and and you learn so

much as well from from mentoring
students.

Yeah, thanks for sharing with
David.

So I would I would just looking
at the watch and clock and I

know also about your your
timeline.

So that was in the entangled
health was what I'm always

inviting is people writing
comments and asking questions.

So I have a few in my pool and
I'm running an algorithm on a

real quantum computer.
So it's running since one hour

in the queue.
So I think I need to I need to

switch back to the simulator.
Yeah, but let's let's maybe just

start the Jingle and I'm going
to read the question for for

you.
And then after after this one,

you can also participate as a as
a guest.

I'll ask one question and then.
Nice.

So just let me run the jingles
over here.

Yeah.
So this is still running.

So we switch back to the
simulation.

Normally it's it's quite quick,
but this time it's running on

IBM Fets for whatever reason.
It's still in, still in queue.

But the other question as I, I
did run before, before I did

recording is quantum question
number 99.

And I think we touched it in the
in the talk.

But the question is from
Charlotta from London.

What's your favorite curiosity
driven journey you've taken?

What's your favorite curiosity?
Driven journey.

I've Oh my so many.
I guess it's.

I, I, I think, I think is the is
when I started working with

quantum computers.
I think that was completely by

curiosity and I knew about it
theoretically when I was doing

the, the bio computer stuff, but
it seemed so distant, you know,

in terms of understanding,
because my, my mathematics is,

is very poor, you know, But in
2017 I discovered that quantum

computers were being built and I
was introduced to, to the

machine that was being built by
Righetti in, in Berkeley.

And I went to Righetti to visit.
And then when I saw what they

were doing there, I said, oh,
this is great.

This is, this is exactly what I
was looking for, you know, at

that particular moment, new new
kinds of hardware, new kinds of

computational processes.
And I think that is completely

by curiosity.
You know, Driven has driven me

to restart.
It's this journey that I am now,

no?
Cool.

So never stop.
Stop being curious.

No, never stop being curious.
And I'm doing other things as

well, driven by curiosity.
But that's I'm going back to the

biocomputing.
Which is interesting.

Yeah, I need, we need to, we
need to follow.

I'm really keen to follow up on
this one and see what you're

doing.
Yeah, it's it's nothing that I

can tell concrete now, but watch
this space.

I think there are now there all
those things that I was doing 15

years ago with the chicken
embryo brain.

You can do that now in much more
sophisticated ways.

And yeah, you see, the the
premise for quantum computing is

that quantum computation is
better to simulate quantum

mechanics than digital
computation, right?

So that this is fine.
And I think, of course, not only

for simulations of physics, but
chemistry.

You know, chemistry is physics,
right?

But then there is biology.
You know, and it makes complex.

Biology emerge from chemistry,
but it's much more than

chemistry and I think my this is
my pet theory that understanding

the brain and making things
intelligent will be much better

to use bio computers than
quantum computers.

Don't tell them.
Quantum computers are good for

chemistry, for the basic level,
for the low level.

But then when you have the
biological emergent properties

like life, consciousness,
adaptation, these are things

that I think we will stumble in
the same wall that quantum

mechanics is stumble when you
know with digital computers.

I think, I think the great
breakthroughs in AI will be when

we have bio computers working on
top of quantum computers.

And this is where I am where I
am going now.

Yeah, that's we need to work on
that together by the way, let's

make it.
But like we take it offline that

cool.
So as a guest, you also have the

question to be to contribute
your own question because I'm

sometimes when you're on the
other side of the mic and being

interviewed.
So the host is just as stupid to

ask these questions you wanted
to talk about.

So feel free to propose a
question and answer it SO.

OK.
My question is, well, I probably

made the question already.
So my question is theoretically

would a bio computer be more
suitable for simulating biology

than quantum computers?
So that's my question.

And this is my curiosity driven
research.

OK, I'm, I'm not, I'm not a
hardcore scientist.

I'm not a hardcore computer
scientist.

Now I know these things, but,
but my research is, is driven by

curiosity.
And this is where my curiosity

is leading to.
And and that's the question, you

know, and my answer is that I
think that bio computers are the

next, the next thing now.
And, and I, I need to deviate a

bit from the roads and ask and
then follow up question to this

one because do do you think
it's, it's, I mean, we have seen

normal mushrooms, so museum
finding the optimal path or

whatever.
And, and we've, we've seen,

we've seen soap bubbles finding
the shortest path, whatever.

So analog computing and we've
seen, I mean either digital or

even analog neuromorphic, Yeah,
infrastructure, yeah.

But you're really talking about
living cells.

That's right.
That's what I'm talking to.

Yeah.
I mean talk.

When I say bio computer, I'm
talking about living neurons,

computers, right?
Chips with neurons, chips with

neurons.
That's what I'm trying to.

Chips with chicks.
Yeah.

So, yeah, this is, this is what,
what I am interested.

And, and at the end of the day,
I, I don't think these different

architectures, you know, are to
be separate from each other.

I think there are levels in, in
the same way that we have, you

know, scientific levels, we have
physics, chemistry, biology,

psychology, sociology.
Now you have these many levels,

I think computational processes
will need to mirror these

levels, you know, the
fundamental level.

Of course we have quantum
mechanics, but then there are

things that we can do very well
already with the digital level.

So quantum mechanics, digital,
then maybe biological and and

then there may be other things
and we, we don't know, you know,

people even talk about chemical
computers, no chemical reactions

doing computations for you.
This is more or less what people

try to do with quantum today.
But there there were things in

the past like these well known
reaction Belousov, Zabotinsky

reaction where you put 2
reactors and then.

And then it's getting these.
These patterns that are

psychical, then you can model
problems in these patterns and

you know so that there is no
reason why a computer needs to

be with a fixed fixed
substratum.

Cool.
So this was really interesting.

Eduardo.
I'm I'm just looking at the

watch.
I'm saying I I always loved my

guests to have the last word.
I'm I'm always saying goodbye

now.
But if you like to share some

closing statements that you
think we as a take home message.

Yeah, Well, really, thank you so
much for the opportunity to have

this chat.
You know, I, I think it is this

kind of dialogue.
It's very, it's very nice

because it, it, it brings to
mine same many, many ideas.

And and you know, it's by
talking that I think we, we can

come up with new ideas and new
curiosities.

If you say.
But yeah, I think my final

thought is creativity is, I
think, a very important human.

Characteristic and trying to get
machines to to simulate

creativity.
I think it's a bit of a waste of

time, but I think to get
machines to help us to be

creative.
That is the key because at the

end of the day, you know, we, we
need to to think about humans,

to think about ourselves and,
and not about terminating

ourselves, but actually, you
know, making ourselves better,

better people.
And I think that, you know,

technology are instruments for
that.

Of course there are bad actors
for everything.

So there are bad, bad people,
but you know, but we need to

think about making good out of
what we are doing.

Yeah, dear friends, we are now
entangled.

Thanks for listening and for
being part of this experiment at

the intersection of quantum tech
and health.

If today's conversation sparked
an idea, a critique, or

triggered a question, please
share it.

And maybe your question is
selected in a future episode out

of the growing pool.
And you know the game.

If you like it, please
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entangle further by passing this
episode to someone you think

should join the gang.
Until next time, stay kind, stay

curious, and keep exploring the
unseen with entangled health.