03/10/2026Sur le calculateur quantique
Le monde quantique est un monde ou les choses (les entittés), sont plus petites qu’un atome.A cette échelle elles ne se comportent pas de la meme façon que nous le voyons à notre échelles.
Les calculateurs quantiques ont beaucoup depotentiel, mais à quelle distance sommes nous des limites de celui-ci ?
On trouve ici une anlyse plus précise dela question
Rappelons ququ bit; l’unité de base dans le cacul quantique, peur avoir les valeurs limites de zéro et de un mais aussi une infinité de valeurs entre ces deux unités. Celles-ci peuvent se superposer ou changer à tout moment, en conséqunce de la fragilité des états quantique. On parle alors de réduction de la fonction d’ondes ou d’effondrement du paquet d’ondes. Dans ce cas, une erreur se produit dans le calcul sans avertissement Les risques d’erreurs sont aujourd’hui d’environ 1/100 . En pratique, cela rend tout calcul impossible
Mais pour amélioret le rendement d’un ordinatur quantique de mille, il est apparu plus rentable au lieu d’augmenter le rendement de chaque qubit individuel de mille, d’assembler mille de ces qu-bits non modifié avec un code de correction d’erreur efficace jusqu’à mille erreur.On nomme alors cet assessemblage de 1000 qubit un qubit logique –
A suivre. Non traduit-
Rather than make each qubit a billion times better, the agreed-upon approach is to make them about 10-100 times better, then assemble these qubits together with an error-correcting code. The individual qubits on the chip are known as physical qubits, and connecting together 100s or 1000s of physical qubits can give one logical qubit. Without improving the quality of the individual physical qubits, the error rate of the logical qubits can be reduced exponentially just by bundling more physical qubits into each one. The only catch: the physical qubit error rate must be lower than a certain threshold before the code will function at all.
A quantum computer that uses error correction to push qubit and gate errors to 0 is called fault-tolerant.
The front-runner for a quantum error-correcting code is called a surface code. The blue region in the chart is where physical qubits are good enough to form logical qubits in a surface code. After some feedback, I added a fade to the bottom of that region, since we’re not exactly sure what the threshold is (very likely between 0.1% to 1%, but it depends on what kind of errors happen).
We’re very close to this! However: If we need 100 physical qubits to make a single logical qubit, then that increases our qubit requirements by a factor of 100. And 100 is an underestimate: longer algorithm’s like Shor’s algorithm (to break RSA) likely require more than 1000 physical qubits per logical qubit.
There has been some talk about quantum computers reaching a point where they cannot be simulated classically. Quantum computers have already passed this point, since classical computers can only simulate about 40 qubits (each qubit doubles the time or memory complexity of the simulation, so even huge classical computers can’t go much higher than this).
However, once we start bundling physical qubits into logical qubits, then the number of qubits which are actually useful (the logical qubits) is much lower than the number of physical qubits. Even if we have 40,000 physical qubits, if we only get 40 logical qubits once we bundle them together for error correction, then any algorithm on those logical qubits is still within reach of classical computers.
The yellow-///-striped region of the chart is where we can’t simulate all the physical qubits, but there are so few logical (error-corrected) qubits that we can classically simulate any error-corrected algorithm.
The bottom left region is bad for quantum computers: if the gate error is 10-3, we can only run about 1000 gates on the physical qubits, probably not enough to do anything useful. But we can’t make enough logical qubits to do anything useful, either!
Once we have decent error rates (say, 10-3) then we just need to build lots of qubits and we can start doing useful things. However, the number of qubits for these fault-tolerant algorithms can be very high.
The green line shows the minimum requirements for a chemical simulation of FeMoCo, a molecule involved in producing ammonia from atmospheric nitrogen.
The red lines show the minimum requirements to run Shor’s algorithm to break RSA keys of various sizes.
What about to the left of that green line? If you had 10,000 qubits but 1 in 1000 gates caused an error (i.e., 100x more qubits and 10x less error than today), could you still do something useful? We’re not sure. There are promising approaches, mainly variational quantum algorithms, which can be more robust against noise and can use error reduction techniques with lower overheads but less benefit than full fault-tolerance. The problem with these is that there are few provable guarantees on their effectiveness. They might work really well, or they might not, and maybe we won’t even know until we can actually build a quantum machine and try them. There is a lot of research in this area, under the term “NISQ”: Noisy Intermediate Scale Quantum computers.
The green-\\\-striped region shows where we can solve some chemistry problems using quantum computers, without needing full error correction. The green dots are resource counts for some specific problems.
From this chart, here is my opinion on the prospects of quantum computing:
- Outside of the green striped region and to the left of the green line, quantum computers might be useless. This is a region where we cannot run any quantum algorithm which outperforms classical computers. Though, we may discover new algorithms which could run on a quantum computer in this region.
- We need Moore’s-law type scaling for quantum computers to ever be useful. This is a log plot. If we ever want to get to the regions on the top or the right, we need to be scaling exponentially in some dimension.
- There may be a sharp transition from “quantum computers are useless” to “quantum computers break RSA”. If we do get exponential scaling, then the distance we have to cover from here to the green line (the first useful application on this chart) is much farther than the distance from there to where all reasonable RSA sizes are broken.
- The green-striped region will probably move down.
- First, it’s the most outside of my expertise and the most likely for a paper that I don’t know about.
- Second, there is a lot of research in this area.
- Third, these are exactly the kind of problems where I would expect the real-world performance of algorithms to vastly exceed the worst-case performance of a theoretical analysis. But we won’t get to see that real-world performance until the big quantum computers are built!
- The red lines probably won’t move much. Unlike with variational problems, we can estimate the runtime and success probability of Shor’s algorithm with known algorithms very precisely. Moreover, Shor’s algorithm is mainly just modular exponentiation, meaning that it’s about as hard for a quantum computer to use RSA as it is to break RSA. An asymptotic improvement is highly unlikely, though small algorithmic improvements and better codes will probably develop and shift the curves a bit to the left. Also, variational algorithms won’t break RSA.
- A big breakthrough could change this whole chart – but a big breakthrough could also break factoring classically. If someone comes up with a code much better than the surface code, then this all radically changes; if someone beats O(n2/log n) multiplication on a quantum computer, the red lines move. But these would be big innovations; we could also get a polynomial-time classical factoring algorithm. How do we decide which scientific breakthroughs are plausible, and which aren’t?
- Even though quantum computers are far away, you should still replace RSA and ECC. Michele Mosca pointed out the main problem: Suppose quantum computers that can break RSA-2048 are 30 years away. If it will take 10 years to standardize new cryptography, 10 years for your organization to implement it, and you need your secrets to remain safe for 12 years, then you’re already 2 years too late, since someone can record your encrypted data and break it later. Basically, it’s a race between the world’s most brilliant quantum engineers and the world’s laziest sysadmins, where the sysadmins have about a 30 year headstart.
If you want a more thorough survey of quantum computing, I recommend John Preskill’s excellent survey, where he coins the term « NISQ ».
____________________________________________
Quantum computers have a lot of potential, but how far are we from that potential? When will all of our RSA keys be obsolete? When will — as Microsoft hopes – quantum computers solve climate change?
I drew a quick sketch of my impressions of the field on Twitter, and Steve Weis asked for a more detailed treatment. Here is a more accurate picture of that sketch:
(Undoubtedly this chart is still incomplete. If you think some of the points on this chart should move, please send me a message and ideally a citation!)
Some terminology: A qubit is the basic unit of data in a quantum computer, and it can store a 1 or a 0 just like a classical bit, but also superpositions of those two states. A gate is an operation applied to a qubit, such as flipping a 0 to a 1, or changing the quantum phase, or XOR, etc.
To explain this chart: quantum data is very fragile. Most types of qubits today can only hold onto a quantum state for a few microseconds before they lose it – either they drift to another quantum state unpredictably, or they interact with the environment and « collapse » any superposition they had.
Not only that, any gate applied to the qubit has some chance of causing error. Today’s qubits are close to 1% gate error. Imagine if every time you sent a bit through a transistor, there was a 1% chance of flipping the bit. You could hardly perform any computation at all!
Google’s Sycamore chip, famous because it seems to be able to solve a (useless) computational problem faster than any classical computer, can only do about 20 gates in a row before being reduced to useless noise. To break RSA-2048 would require more than 2.1 billion gates in a row.
For quantum computers to be useful, we need more qubits but we also need better qubits.
Rather than make each qubit a billion times better, the agreed-upon approach is to make them about 10-100 times better, then assemble these qubits together with an error-correcting code. The individual qubits on the chip are known as physical qubits, and connecting together 100s or 1000s of physical qubits can give one logical qubit. Without improving the quality of the individual physical qubits, the error rate of the logical qubits can be reduced exponentially just by bundling more physical qubits into each one. The only catch: the physical qubit error rate must be lower than a certain threshold before the code will function at all.
A quantum computer that uses error correction to push qubit and gate errors to 0 is called fault-tolerant.
The front-runner for a quantum error-correcting code is called a surface code. The blue region in the chart is where physical qubits are good enough to form logical qubits in a surface code. After some feedback, I added a fade to the bottom of that region, since we’re not exactly sure what the threshold is (very likely between 0.1% to 1%, but it depends on what kind of errors happen).
We’re very close to this! However: If we need 100 physical qubits to make a single logical qubit, then that increases our qubit requirements by a factor of 100. And 100 is an underestimate: longer algorithm’s like Shor’s algorithm (to break RSA) likely require more than 1000 physical qubits per logical qubit.
There has been some talk about quantum computers reaching a point where they cannot be simulated classically. Quantum computers have already passed this point, since classical computers can only simulate about 40 qubits (each qubit doubles the time or memory complexity of the simulation, so even huge classical computers can’t go much higher than this).
However, once we start bundling physical qubits into logical qubits, then the number of qubits which are actually useful (the logical qubits) is much lower than the number of physical qubits. Even if we have 40,000 physical qubits, if we only get 40 logical qubits once we bundle them together for error correction, then any algorithm on those logical qubits is still within reach of classical computers.
The yellow-///-striped region of the chart is where we can’t simulate all the physical qubits, but there are so few logical (error-corrected) qubits that we can classically simulate any error-corrected algorithm.
The bottom left region is bad for quantum computers: if the gate error is 10-3, we can only run about 1000 gates on the physical qubits, probably not enough to do anything useful. But we can’t make enough logical qubits to do anything useful, either!
Once we have decent error rates (say, 10-3) then we just need to build lots of qubits and we can start doing useful things. However, the number of qubits for these fault-tolerant algorithms can be very high.
The green line shows the minimum requirements for a chemical simulation of FeMoCo, a molecule involved in producing ammonia from atmospheric nitrogen.
The red lines show the minimum requirements to run Shor’s algorithm to break RSA keys of various sizes.
What about to the left of that green line? If you had 10,000 qubits but 1 in 1000 gates caused an error (i.e., 100x more qubits and 10x less error than today), could you still do something useful? We’re not sure. There are promising approaches, mainly variational quantum algorithms, which can be more robust against noise and can use error reduction techniques with lower overheads but less benefit than full fault-tolerance. The problem with these is that there are few provable guarantees on their effectiveness. They might work really well, or they might not, and maybe we won’t even know until we can actually build a quantum machine and try them. There is a lot of research in this area, under the term “NISQ”: Noisy Intermediate Scale Quantum computers.
The green-\\\-striped region shows where we can solve some chemistry problems using quantum computers, without needing full error correction. The green dots are resource counts for some specific problems.
From this chart, here is my opinion on the prospects of quantum computing:
- Outside of the green striped region and to the left of the green line, quantum computers might be useless. This is a region where we cannot run any quantum algorithm which outperforms classical computers. Though, we may discover new algorithms which could run on a quantum computer in this region.
- We need Moore’s-law type scaling for quantum computers to ever be useful. This is a log plot. If we ever want to get to the regions on the top or the right, we need to be scaling exponentially in some dimension.
- There may be a sharp transition from “quantum computers are useless” to “quantum computers break RSA”. If we do get exponential scaling, then the distance we have to cover from here to the green line (the first useful application on this chart) is much farther than the distance from there to where all reasonable RSA sizes are broken.
- The green-striped region will probably move down.
- First, it’s the most outside of my expertise and the most likely for a paper that I don’t know about.
- Second, there is a lot of research in this area.
- Third, these are exactly the kind of problems where I would expect the real-world performance of algorithms to vastly exceed the worst-case performance of a theoretical analysis. But we won’t get to see that real-world performance until the big quantum computers are built!
- The red lines probably won’t move much. Unlike with variational problems, we can estimate the runtime and success probability of Shor’s algorithm with known algorithms very precisely. Moreover, Shor’s algorithm is mainly just modular exponentiation, meaning that it’s about as hard for a quantum computer to use RSA as it is to break RSA. An asymptotic improvement is highly unlikely, though small algorithmic improvements and better codes will probably develop and shift the curves a bit to the left. Also, variational algorithms won’t break RSA.
- A big breakthrough could change this whole chart – but a big breakthrough could also break factoring classically. If someone comes up with a code much better than the surface code, then this all radically changes; if someone beats O(n2/log n) multiplication on a quantum computer, the red lines move. But these would be big innovations; we could also get a polynomial-time classical factoring algorithm. How do we decide which scientific breakthroughs are plausible, and which aren’t?
- Even though quantum computers are far away, you should still replace RSA and ECC. Michele Mosca pointed out the main problem: Suppose quantum computers that can break RSA-2048 are 30 years away. If it will take 10 years to standardize new cryptography, 10 years for your organization to implement it, and you need your secrets to remain safe for 12 years, then you’re already 2 years too late, since someone can record your encrypted data and break it later. Basically, it’s a race between the world’s most brilliant quantum engineers and the world’s laziest sysadmins, where the sysadmins have about a 30 year headstart.
If you want a more thorough survey of quantum computing, I recommend John Preskill’s excellent survey, where he coins the term « NISQ ».
Europe Solidaire 