#Microsoft big quantum error after all how to
#Microsoft big quantum error after all software

Many examples of physical systems are known where such periodic evolution allows new phenomena to occur (see, for example, the well-known Kapitza pendulum). Our recent breakthroughs overcome this issue through a conceptually new perspective on quantum codes (put forward in “ Dynamically Generated Logical Qubits” and “ Boundaries for the Honeycomb code”), where the encoding of the quantum information is not static but rather allowed to periodically evolve in time. The outcomes of these measurements are used to infer the occurrence of errors without destroying the encoded quantum state. While all quantum error correction schemes use frequent measurements to identify errors, the state-of-the-art schemes require complex multi-qubit measurements that can’t be implemented directly in the hardware and must be compiled into native operations at the expense of additional auxiliary qubits and additional timesteps. Unlike most other qubits, our topological qubits employ a measurement-based scheme, where direct measurements between adjacent qubits are the native set of operations. This is particularly true for error correction schemes, which must be tailor-made to exploit the strengths of a given hardware platform. To optimize performance on any quantum computing platform, the circuits must be adapted to the capabilities of the hardware. Our new approaches culminate in an additional tenfold or more reduction to the overhead needed for error correction on topological qubits compared to previous state of the art, opening a viable path toward scaling to a million qubits and beyond.Īdiabatic paths of Hamiltonians, symmetries of topological order, and automorphism codes On top of that, in these papers we introduce a new class of quantum error correction codes, called Floquet codes, which are particularly suited to topological qubits. Topological qubits promise lower error rates than conventional qubits, and as such can perform scalable quantum computation at lower overhead.

#Microsoft big quantum error after all how to

In this post, we’ll share some exciting implications from our recent innovations toward scale-specifically how to perform quantum error correction in our topological quantum computation stack- published in the series of papers listed below. Therefore, any improvements on error correction have enormous positive ripple effects across the entire stack. However, quantum error correction also comes at a significant cost: depending on the quality of the physical qubits, error correction can increase the space requirements of a computation by a factor of several thousand and the time requirements more than tenfold. Without it, no known qubit technology can protect quantum states sufficiently long enough to perform a calculation that can deliver real-world impact. Indeed, such error correction needs to be at the heart of any scalable quantum system.

By encoding the state of a single logical qubit into many physical qubits, quantum error correction (QEC) has the ability to detect and correct most errors that occur on the physical qubits. A crucial technology to overcome this fragility, which is also used in classical digital computing, is error correction.

One of the greatest challenges in building a quantum computer is that quantum states are intrinsically fragile and are quickly destroyed when a qubit couples to its environment, leading to noise.

#Microsoft big quantum error after all software

At Azure Quantum, our full-stack approach and broad expertise across all areas of quantum computation allows us to drive innovation in this space through tight collaboration across theory, hardware, software and systems teams. However, our quest to deliver a general-purpose quantum computer capable of addressing industrial-scale problems will require innovation across every layer of the quantum stack, from materials at the nanoscale to algorithms and applications. In March of this year, we announced our demonstration of the underlying physics required to create a topological qubit-qubits that are theorized to be inherently more stable than existing ones without sacrificing size or speed. Technological innovation that enables scaling of quantum computing underpins the Microsoft Azure Quantum program. Learn more about this sequence of checks in the section “Unlocking a new class of quantum codes” below. This graphic shows the repeating three-step sequence of checks used in Floquet codes. Each circle represents a qubit, and a line between a pair of circles indicates that that check is measured on that time step. The colors indicate the type of operator measured in each check, either XX, YY, or ZZ, so that the type of check measured also changes with time.