The year 2023 could see major advances in quantum computing, with the potential development of quantum computers capable of
surpassing the performance of classical supercomputers on certain tasks. This would represent a major milestone, known as “quantum supremacy”, in which quantum computers demonstrate unambiguous speedup over classical devices.
In this article, we will examine the current state of quantum computing and what it will take to build, test, and operate a large-scale quantum computer in 2023.
Table of Contents
Current State of Quantum Computing
While quantum computing is still in its early stages, significant progress has been made in recent years. Tech giants like IBM, Google, and Microsoft now operate prototype quantum computers with 10-100 qubits (quantum bits). However, to achieve quantum supremacy and solve practical problems, it’s estimated that a quantum computer will need thousands or even millions of qubits. The goal is to build and operate a quantum computer in 2023 with at least 1000 physical qubits.
Several hardware platforms are being pursued for scalable quantum computing, including superconducting circuits, trapped ions, and photonics. Each has its advantages and challenges. Currently, superconducting quantum systems developed by Google and IBM are leading the race in terms of qubit count, but trapped ion and photonic systems also show promise. The winning hardware in 2023 may utilize a hybrid approach.
However, qubit count alone does not determine a quantum computer’s capabilities. A metric called “quantum volume” also accounts for connectivity, gate fidelities, and cross-talk. So two 50-qubit systems could have vastly different quantum volumes. Building a high quantum volume system at 1000+ qubits will be a key goal for 2023.
On the software side, the development of quantum algorithms and applications is still early stages. Useful algorithms like Shor’s algorithm for factoring and Grover’s algorithm for search have been around for decades. Recent progress includes quantum machine learning algorithms and quantum simulation algorithms. More work is needed to develop a practical toolkit of quantum algorithms that show unambiguous speedup over classical computation.
Quantum Error Correction
One of the biggest challenges in scaling up quantum computers is error correction. Qubits are fragile, and quantum operations can introduce errors that lead to decoherence. Quantum error correction uses redundant qubits to detect and correct errors but is costly in terms of qubit overhead. Efficient error correction will be critical for any large-scale quantum computer built in 2023 and beyond.
Requirements for Building a 1000+ Qubit Quantum Computer
To achieve the goal of demonstrating an operational 1000+ qubit quantum computer by the end of 2023, a massive coordinated effort over several fronts will be required.
On the hardware front, the quantum processor itself will need to operate with high qubit count, connectivity, and gate fidelities. Cryogenic engineering will be critical to enable chips with 1000+ qubits. Investments in hardware infrastructure will be needed to scale up fabrication, packaging, and cabling capabilities.
A robust software stack will need to be developed including compilers, simulators, control systems, and cloud integration tools. Efficient scheduling algorithms will be needed to optimize running quantum programs. Software frameworks that simplify writing quantum programs for developers will be important.
Work will be needed both on developing new quantum algorithms and applications, as well as porting existing applications to run effectively on NISQ (Noisy Intermediate Scale Quantum) devices. Focus areas could include quantum chemistry, optimization, machine learning, and finance. Building interfaces with popular software like Tensorflow could accelerate adoption.
Providing cloud access mechanisms that allow researchers and developers to easily program the quantum system will be critical. This will fuel innovation and help improve quantum algorithms. Cloud access also facilitates benchmarking between different quantum hardware platforms.
All of the above will require assembling a world-class cross-disciplinary team including physicists, engineers, programmers, and technicians. Investments in training programs to build up quantum expertise will pay dividends. Partnerships between academia, national labs, and corporations can pool resources and knowledge.
Testing and Benchmarking a 1000+ Qubit System
Once built, the 1000+ qubit system will need to be thoroughly tested and benchmarked. This will involve running test algorithms to characterize the system’s quantum volume, jitter, crosstalk, error rates, and qubit connectivity. Comparisons will be made between simulated performance and hardware results.
As mentioned earlier, quantum volume is a hardware-agnostic metric that combines factors like qubit number, connectivity, and gate fidelities. The system should demonstrate a quantum volume in the range of 32-64 to establish itself as a world-leading superconducting platform.
Randomized benchmarking will assess the error rates of random sequences of gates across the system. Average gate fidelities > 99% will be targeted to support error correction.
To claim quantum supremacy, the system must be rigorously benchmarked against classical computing systems. It should decisively perform certain sampling tasks beyond the reach of any existing or projected supercomputer within a reasonable time. This milestone will mark a major achievement.
Applications and Limitations of Near-Term Quantum Computers
While quantum computers in 2023 will be unlikely to outperform classical systems on most practical applications, we may see early indications of quantum advantage in a few domains such as:
- Quantum chemistry – Estimating molecular properties with algorithms like VQE
- Optimization – Finding optimal solutions from combinatorial sets
- Machine learning – Classification, clustering, or dimensionality reduction
- Financial analysis – Pricing options or portfolio optimization
However, limitations due to low qubit counts, gate error rates, and lack of error correction will persist in 2023. Areas like codebreaking, pharmaceutical design, and climate modeling will likely remain out of reach without major increases in scale. Practical applications may not emerge until the late 2020s at the earliest.
In summary, 2023 has the potential to be a landmark year for quantum computing. Operating a superconducting quantum processor with 1000+ qubits could mark the achievement of quantum supremacy. However, this will require massive coordinated efforts across hardware, software, engineering, and application development. If achieved, this milestone will generate excitement and accelerate further progress and investment in quantum computing. We are on the cusp of a new computing paradigm that could one day transform information processing across many industries and domains of science.
What are the leading hardware platforms for quantum computing?
The leading hardware platforms are currently superconducting circuits, trapped ions, and photonics. Each has relative strengths – superconducting qubits have higher counts, trapped ions have long coherence times, and photons are good for communication. A hybrid system combining multiple platforms may emerge as the winner.
How many physical qubits are needed for quantum supremacy?
There is no firm number, but most experts estimate that thousands or even millions of qubits will be needed for unambiguous quantum supremacy over classical supercomputers. A system with 1000+ qubits is a reasonable goal for 2023.
What are the main challenges in scaling up quantum computers?
Some of the biggest challenges are reducing noise and errors, developing fault-tolerant error correction, increasing qubit connectivity, developing software and applications, maintaining coherence during qubit operations, and managing cross-talk as qubit count increases.
What is quantum volume and why does it matter?
Quantum volume accounts for factors like the number of qubits, connectivity, and gate fidelities to provide a hardware-agnostic metric of a quantum system’s capabilities. It gives a more comprehensive measure than qubit count alone when comparing systems.
What applications will be possible on near-term quantum computers?
In the near-term, quantum computers may show advantages in a limited number of domains like quantum chemistry, optimization, machine learning, and finance. But limitations will persist until larger, error-corrected systems can be built.
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