Abstract Quantum computers have advanced rapidly in qubit count and gate fidelity. However, large-scale fault-tolerant quantum computing still relies on quantum error correction code (QECC) to suppress noise. Manually or experimentally verifying the fault-tolerance property of complex QECC implementation is impractical due to the vast error combinations. This paper formalizes the fault-tolerance of QECC implementations within the language of quantum programs. By incorporating the techniques of quantum symbolic execution, we provide an automatic verification tool for quantum fault-tolerance. We evaluate and demonstrate the effectiveness of our tool on a universal set of logical operations across different QECCs.

The rapid development of quantum computing technology has led to an increasing need for efficient and accessible programming tools that can bridge the gap between classical and quantum computing paradigms. In this paper, we introduce the Intel®Quantum SDK, a powerful software development kit designed to facilitate hybrid quantum-classical programming using the C++ Quantum Extension. The SDK provides a comprehensive set of tools and libraries that enable developers to harness the power of quantum computing while leveraging their existing knowledge of classical programming techniques. By incorporating quantum algorithms and operations within a familiar C++ framework, the Intel Quantum SDK empowers programmers to create innovative hybrid applications that can tackle complex problems and drive advancements in various fields, including cryptography, optimization, and materials science. This paper will guide readers through the essential concepts, features, and capabilities of the Intel Quantum SDK. This seamless integration of quantum and classical programming paradigms paves the way for the development of innovative and efficient solutions to complex computational challenges, ultimately driving the advancement of quantum computing technology and its real-world applications.

The emergence of quantum computers as a new computational paradigm has been accompanied by speculation concerning the scope and timeline of their anticipated revolutionary changes. While quantum computing is still in its infancy, the variety of different architectures used to implement quantum computations make it difficult to reliably measure and compare performance. This problem motivates our introduction of SupermarQ, a scalable, hardware-agnostic quantum benchmark suite which uses application-level metrics to measure performance. SupermarQ is the first attempt to systematically apply techniques from classical benchmarking methodology to the quantum domain. We define a set of feature vectors to quantify coverage, select applications from a variety of domains to ensure the suite is representative of real workloads, and collect benchmark results from the IBM, IonQ, and AQT@LBNL platforms. Looking forward, we envision that quantum benchmarking will encompass a large cross-community effort built on open source, constantly evolving benchmark suites. We introduce SupermarQ as an important step in this direction.

Trapped-ion qubits are a leading technology for practical quantum computing. In this work, we present an architectural analysis of a linear-tape architecture for trapped ions. In order to realize our study, we develop and evaluate mapping and scheduling algorithms for this architecture. In particular, we introduce TILT, a linear “Turing-machinelike” architecture with a multilaser control “head,” where a linear chain of ions moves back and forth under the laser head. We find that TILT can substantially reduce communication as compared with comparable-sized Quantum Charge Coupled Device (QCCD) architectures. We also develop two important scheduling heuristics for TILT. The first heuristic reduces the number of swap operations by matching data traveling in opposite directions into an “opposing swap.”, and also avoids the maximum swap distance across the width of the head, as maximum swap distances make scheduling multiple swaps in one head position difficult. The second heuristic minimizes ion chain motion by scheduling the tape to the position with the maximal executable operations for every movement. We provide application performance results from our simulation, which suggest that TILT can outperform QCCD in a range of NISQ applications in terms of success rate (up to 4.35x and 1.95x on average). We also discuss using TILT as a building block to extend existing scalable trapped-ion quantum computing proposals.

Compiling high-level quantum programs to machines that are size constrained (i.e. limited number of quantum bits) and time constrained (i.e. limited number of quantum operations) is challenging. In this paper, we present SQUARE (Strategic QUantum Ancilla REuse), a compilation infrastructure that tackles allocation and reclamation of scratch qubits (called ancilla) in modular quantum programs. At its core, SQUARE strategically performs uncomputation to create opportunities for qubit reuse.Current Noisy Intermediate-Scale Quantum (NISQ) computers and forward-looking Fault-Tolerant (FT) quantum computers have fundamentally different constraints such as data locality, instruction parallelism, and communication overhead. Our heuristic-based ancilla-reuse algorithm balances these considerations and fits computations into resource-constrained NISQ or FT quantum machines, throttling parallelism when necessary. To precisely capture the workload of a program, we propose an improved metric, the “active quantum volume,” and use this metric to evaluate the effectiveness of our algorithm. Our results show that SQUARE improves the average success rate of NISQ applications by 1. 47X. Surprisingly, the additional gates for uncomputation create ancilla with better locality, and result in substantially fewer swap gates and less gate noise overall. SQUARE also achieves an average reduction of 1. 5X (and up to 9. 6X) in active quantum volume for FT machines.

We identify an important asymmetry in physical DRAM cells that can be utilized to prevent RowHammer attacks by adding 18 lines of code to modify the OS memory allocator. Our small modification has a powerful impact on RowHammer's ability to bypass memory protection mechanisms and achieve a successful attack. Specifically, we identify two types of DRAM cells: true-cells and anti-cells. In a true-cell, a leaking capacitor will induce a '1'->'0' error, while in anti-cells, errors flow from '0'->'1'. We then create DRAM cell-type-aware memory allocation which enables a "monotonicity property" for a given data object. The monotonicity property is able to counter RowHammer attacks (and, to a broader extent, other memory attacks) by allocating only one type of cells for an object, thereby restricting error direction. We apply the monotonicity property to pointers in page tables by placing all page tables in true-cells that are above a "low water mark". We show that this approach successfully defends against page-table-based privilege escalation RowHammer attacks. Using established RowHammer-induced bit-flip error statistics, we provide proofs of the soundness and completeness of our technique and show that with our technique only one out of 2.04x10 5 systems is vulnerable to the attack, and the expected attack time on the vulnerable system is 231 days. We also provide application performance results from prototypes implemented through modifications to Linux kernels. Our cross-layer approach avoids undesirable energy cost, hardware changes, performance overhead, and high software complexity associated with prior countermeasures.

Quantum circuit simulations are critical for evaluating quantum algorithms and machines. However, the number of state amplitudes required for full simulation increases exponentially with the number of qubits. In this study, we leverage data compression to reduce memory requirements, trading computation time and fidelity for memory space. Specifically, we develop a hybrid solution by combining the lossless compression and our tailored lossy compression method with adaptive error bounds at each timestep of the simulation. Our approach optimizes for compression speed and makes sure that errors due to lossy compression are uncorrelated, an important property for comparing simulation output with physical machines. Experiments show that our approach reduces the memory requirement of simulating the 61-qubit Grover's search algorithm from 32 exabytes to 768 terabytes of memory on Argonne's Theta supercomputer using 4,096 nodes. The results suggest that our techniques can increase the simulation size by 2~16 qubits for general quantum circuits.