State of the Art¶
Hardware¶
NISQ era (Noisy Intermediate-Scale Quantum Computers) [108]
- Superconducting qubits: current state of play [R10]
Challenges (scale, quality, speed):
number of qubits: more qubits on a chip, or “extend quantum systems with quantum optical channels” [M15], see also Entanglement across separate silicon dies [49]
quality of qubits: error-rate, coherence time
manufacturing: e.g. avoid defects introducting splitting of levels [M33]
connectivity of qubits: strong impact on performance, (but simple ladder connectivity may be good enough for current hardware [62] [M24])
data I/O: performance of loading classical data, faster read out (measurements) [M24]
control electronics: “Reduce the wiring: move the controller to cryogenic temperature (4K) - cryogenic CMOS” [M8]
Software¶
- ASM / Machine Instructions
- currently Variational Quantum Algorithms or Quantum Annealing:
see Algorithms, also Quantum Approximate Optimization in QAOA.
Challenges:
Quantum Advantage¶
Also refered to as Quantum Supremacy e.g. by Google. The term Quantum Advantage was preferred by IBM.
- Google’s 2019 Quantum Supremacy claim with 53 qubits [7] for an academic problem.
- More from Google AI Quantum Research in the dedicated section: Quantum Advantage
Estimate of the number of qubits to achieve Quantum Supremacy (2020) [30]
“An IQP circuit with 208 qubits, a QAOA circuit with 420 qubits, and a boson sampling circuit with 98 photons each would require at least one century to be simulated using a classical simulation algorithm”
- Focus beyond Quadratic Speedups for Error-Corrected Quantum Advantage (2021) [14]
“We discuss conditions under which it would be possible for a modest fault-tolerant quantum computer to realize a runtime advantage by executing a quantum algorithm with only a small polynomial speedup over the best classical alternative. […] We conclude that quadratic speedups will not enable quantum advantage on early generations of such fault-tolerant devices unless there is a significant improvement in how we realize quantum error correction.”
- About quantum chemistry: Gate-count estimates for performing quantum chemistry on small quantum computers? (2014) [131]
- About quantum machine learning: Quantum advantage in learning from experiments (2022) [65]
- About optimization problems: Where is the quantum advantage? (2021) [M32]
- About quantum annealing: When can Quantum Annealing win? [31] (2016), see also State of the art of AQC.
The important questions to investigate:
A list of problems solved more efficiently on quantum computers.
For what problems can we expect an exponential speedup?
Balanced Opinions¶
“Quantum Computing: A bubble ready to burst?”, Nov-11-2020 [M6],
“Will Quantum Computing ever live up to its hype?”, Apr-20-2021 [M17]
“Quantum computing has a hype problem”, Mar-28-2022 [M12]
“Quantum Computing will change our lives. But be patient, please”, Dec-14-2022, [M36]
Further readings
“Status of quantum computer development” by the German Federal Office for Information Security [M42]