Quantum computing

Engineers and scientists at QCi have spent over a decade innovating elements and components for quantum photonics devices and developing a novel approach for encoding and processing quantum information. From its inception, this approach has been aimed at developing an efficient, practical, scalable, and affordable methodology for solving a large variety of real-world problems. The resulting methodology is called “Entropy Quantum Computing” or "EQC".

This page will give you an overview of this exciting new technology. For a technical deep dive into our entropy quantum computing research click here.

EQC 1

Computing for some of the world's hardest problems

What they can do

Dirac systems are portable, low power, room temperature Hybrid Analog Machine with Quantum Optics and Digital Electronics, designed to solve complex optimization problems ranging from binary (qubit) to discrete number (qudit) optimization and beyond.

Binary and integer optimization solver
Non-convex optimization
Many body interactions

Example problems

Learn more about how the Dirac systems are being used in the field.

Logistics solutions using quadratic assignment problem

Risk-based UAS flight trajectory optimization

Portfolio optimization equal weights dirac1


Details

Dive a little deeper into how Dirac systems work. To see our research and publications, click here.

In quantum information processing, loss and noise are usually detrimental and must be minimized. This is why quantum systems using atomic and alike qubits must be hosted in cryogenic vacuum chambers, and why photon loss is the roadblock to quantum communications and computing. This requirement translates to exceeding challenges in quantum system manufacture and operations, and has been the bottleneck preventing the scaling up of the qubit number and connectivity. 

With entropy quantum computing, we flip the coin around. Instead of trying to avoid loss and noise, we harness them to build quantum machines whose capacity and speed outmatch existing computing modalities. 

This fundamentally new quantum computing approach is called Entropy Quantum Computing (EQC). It roots deeply in the intriguing principles of quantum mechanics. First, loss or decoherence of a quantum state occurs through its coupling to an entropy source with many degrees of freedom. The apparent diminishing of quantum characteristics as a result is just a statistically averaged manifestation of many possible outcomes of such coupling. Second, vacuum is never quiet, although it does not appear to contain any energy or particle. There are, in fact, enormous amounts of random fluctuations occurring at all times in each of the vacuum mode.

EQC is conceived and developed with those intriguing quantum principles. Rather than trying to create and manipulate pristine qubits isolated from the environment, EQC utilizes loss and decoherence, and turns entropy into super-power fuels of its computing engine. In sharp contrast to any existing quantum platforms, there is no need for cryogenic or isolated housing, and the implementation can use integrated photonics, leading to SWAP-C friendly devices, just like regular PC’s.


How it works

Our system consists of 3 principle components: an amplifier, a mixer, and a loss medium. These building blocks are realized using a combination of nonlinear optical components and photonic integrated circuits, coupled with FPGAs. The system then does the following steps:

  1. Create a set of many states: in our current architecture these states reside in an optical cavity.

  2. Populate those states: this is done by coupling the physical system to an entropy bath

  3. Introduce some gain & loss into the system

  4. Modulate the loss using the Zeno effect: different embodiments of our hardware do this differently, either electrically, or by a hybrid electrical-optical modulation, or purely optically

  5. Wait for the solution: wait until the loss is approximately matched to the gain of the marked / desired state, i.e., our solution

how it works

Publications

All offerings are rooted in our scientific publications. To see an exhaustive list of our publications, click here.

Ultra-efficient frequency conversion in quasi-phase-matched lithium niobate microrings

Interaction- and measurement-free quantum Zeno gates for universal computation with single-atom and single-photon qubits

Observation of distinct phase transitions in a nonlinear optical Ising machine


Product overview

Summary

Dirac-1 is a portable, low power, and room temperature qubit entropy quantum computer (EQC). Dirac-1 solves problems of Objective Function Minimization and Maximization for binary optimization by finding the ground state energy of a complex system with many inter-correlated variables.

These problems correspond to minimizing or maximizing the expected return of the objective function:

Dirac-1 Equation

where ViV_i is the value of each variable, CiC_i is the linear coefficient of each variable, which is a real number that can be positive, negative, or zero, JijJ_{i j} is the coupling coefficient of two variables, which can be any real number

Specifications

Type

Qubit (superposition of 0 and 1)

Maximum size of variables

N = 11,000

Connectivity

All-to-all

Operating temperature

25 °C / 77 °F (room temperature)

Power consumption

<80 W

Physical size

Contained in a 3U rack-mountable unit

Order of correlation

Any types of second-order correlations, where interactions between qubits can be repulsive (positive correlation) or attractive (negative correlation)

Summary

Dirac-2 is a portable, low power, and room temperature qudit entropy quantum computer (EQC). Dirac-2 solves problems of Objective Function Minimization and Maximization for integer optimization by finding the ground state energy of a complex system with many inter-correlated variables. These problems correspond to minimizing or maximizing the expected return of the objective function:

Dirac-1 equation

under the constraint of a fixed resource R=i=1NViR=\sum_{i=1}^NV_i where ViV_i is the value of each variable, CiC_i is the linear coefficient of each variable, which is a real number that can be positive, negative, or zero, JijJ_{i j} is the coupling coefficient of two variables, which can be any real number.

Specifications

Type

Qudit of 64 dimensions

Maximum size of variables

N = 1,000

Connectivity

All-to-all

Operating temperature

25 °C / 77 °F (room temperature)

Power consumption

<80 W

Physical size

Contained in a 3U rack-mountable unit

Order of correlation

Any types of second-order correlations, where interactions between qudits can be repulsive (positive correlation) or attractive (negative correlation)

Summary

Discrete optimization problems involve identifying the most favorable solution from a finite and discrete set of possibilities. Some prominent examples include the traveling salesman problem, knapsack problem, graph coloring, spanning tree, matching, set covering, and set packing.

These problems are applicable to a wide array of fields, including scheduling, logistics, network design, drug discovery, and data analysis. Many discrete optimization problems are NP-hard, that is, no known algorithm can efficiently solve all instances of the problem in polynomial time relative to the input size. Consequently, as the problem size increases, the time required to find an exact optimal solution may grow exponentially. Advancements in hardware are necessary to meet these increasing computing demands. However, the saturation of the physical boundaries of semiconductor miniaturization poses a significant obstacle to further reducing transistor sizes. Simple parallel processing is not power efficient and does not meet the exponentially growing demand in data processing speed and capacity. Therefore, a logical progression is shifting away from universal Turing Machines and exploring alternative computing methodologies for specific tasks.

Dirac-3 solves the following objective function:

dirac-3 objective function

Specifications

Solver type

Constrained Discrete Number Optimization

Hardware type

Hybrid Analog Machine with Quantum Optics and Digital Electronics

Maximum number of variables

949

Order of correlation

Any types of first- through
fifth-order correlations,
where the interaction amongst variables
can be repulsive (positive correlation)
or attractive (negative correlation).

Connectivity

All-to-all

System power consumption

under 100 Watts

Storage temperature

-25◦C to 85◦C

Operating temperature

20◦C to 27◦C

Maximum rate of change

2◦C per hour

Software requirement

On-prem: eqc-direct software package, Python 3.10.6 (recommended)

Cloud: qci-client software package

OS requirement

On-prem

: Linux (recommended)

Dimension

5U rack-mounted


Get started now

Trial cloud access

Get access to entire line of Dirac device, including Dirac-3, with enough time allocation to test basic capabilities.

This includes:

  • 10 minutes of time allocation

Free


Hourly cloud access

Purchase access to the entire line of Dirac devices, including Dirac-3, on an hourly basis.

This includes:

  • 60 minutes of time allocation

$1,000/hour

$4,000/5 hours


Cloud + concierge

Work directly with our team of applications scientists to formulate, optimize, and execute your problems.

This includes:

  • 60 minutes of time allocation

  • 1 hour of scheduled, dedicated access to one of our Dirac machines

  • 1 hour session with a QCi Application Scientist for technical support and application coaching

$2,000/hour

$10,000/6 hours


On prem

Install Dirac-3 within your local premises.

  • Eliminate queueing delays from shared usage

  • Achieve fastest loading speeds with EQC-Direct

  • Server room temperature operation

  • Superior SWaP-C characteristics

Starting at $300,000