Optimized Surface Code Communication in Superconducting Quantum Computers

Quantum computing (QC) is at the cusp of a revolution. Machines with 100 quantum bits (qubits) are anticipated to be operational by 2020 [googlemachine,gambetta2015building], and several-hundred-qubit machines are around the corner. Machines of this scale have the capacity to demonstrate quantum supremacy, the tipping point where QC is faster than the fastest classical alternative for a particular problem. Because error correction techniques will be central to QC and will be the most expensive component of quantum computation, choosing the lowest-overhead error correction scheme is critical to overall QC success. This paper evaluates two established quantum error correction codes---planar and double-defect surface codes---using a set of compilation, scheduling and network simulation tools. In considering scalable methods for optimizing both codes, we do so in the context of a full microarchitectural and compiler analysis. Contrary to previous predictions, we find that the simpler planar codes are sometimes more favorable for implementation on superconducting quantum computers, especially under conditions of high communication congestion.Comment: 14 pages, 9 figures, The 50th Annual IEEE/ACM International Symposium on Microarchitectur

Software Techniques to Mitigate Errors on Noisy Quantum Computers

Quantum computers are domain-specific accelerators that can provide a large speedup for important problems. Quantum computers with few tens of qubits have already been demonstrated, and machines with 100+ qubits are expected soon. These machines face significant reliability and scalability challenges. The high hardware error rates limit quantum computers. To enable quantum speedup, it is essential to mitigate hardware errors. Our first work exploits the variability in the error rates of qubits to steer more operations towards qubits with lower error rates and avoid error-prone qubits. Our second work looks at executing different versions of the programs tuned to cause diverse mistakes so that the machine is less vulnerable to correlated errors, thereby making it easier to infer the correct answer. Our third work looks at exploiting the state-dependent bias in measurement errors (state 1 is more error-prone than state 0) and dynamically flips the state of the qubit to measure the stronger state. We perform our evaluations on real quantum machines from IBM and demonstrate significant improvement in the overall system reliability.Ph.D

Quantum computing for finance

Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. We present a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning. This Review is aimed at physicists, so it outlines the classical techniques used by the financial industry and discusses the potential advantages and limitations of quantum techniques. Finally, we look at the challenges that physicists could help tackle