Loading in 5 sec....

Channel-Adapted Quantum Error CorrectionPowerPoint Presentation

Channel-Adapted Quantum Error Correction

- By
**paul2** - Follow User

- 183 Views
- Updated On :

Channel-Adapted Quantum Error Correction. Andrew Fletcher QEC ‘07 21 December 2007. Channel-adapted Quantum Error Recovery (QER). Encoder. Channel. Recovery. QEC scheme specifies Encoder and Recovery Generic methods do this independently of channel

Related searches for channel-adapted quantum error correction

Download Presentation
## PowerPoint Slideshow about 'channel-adapted quantum error correction' - paul2

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

Channel-adaptedQuantum Error Recovery (QER)

Encoder

Channel

Recovery

- QEC scheme specifies Encoder and Recovery
- Generic methods do this independently of channel

- Channel-adapted QER selects given encoder and channel, seeking to maximize entanglement fidelity
- Essentially, maximize probability of correct transmission

- Optimization problem can be solved exactly using a semidefinite program (SDP)

Quantum Operations

- What are valid choices for the recovery ?
- Quantum operations are completely positive and trace preserving (CPTP)
- Standard expression for operation uses Kraus form
- Many sets of operators correspond to the same mapping

- Less common Choi matrix is more convenient
- Use Jamiolkowski isomorphism
- Operation is uniquely specified by a positive operator
- Constraints:

- Entanglement fidelity has simple form with Choi matrix

Optimum Channel-adapted QER

- The optimal recovery operation has a nice form
- Linear objective function:
- Linear equality constraint:
- Semidefinite matrix constraint:

- Optimization problem is a semidefinite program
- is the Choi matrix for the channel and input
- Convex optimization problem with efficient solution

Channel-adapted recoveries yielded better entanglement fidelity in both examples

Improved performance even for lower noise channels

Channel-adaptation extended the region where error correction was effective

Doubled for amplitude damping

QER Example: [5,1] Code,Amplitude Damping Channel[4,1] Channel-adapted Code of fidelity in both examplesLeung et. al.

- Channel-adaptation can make more efficient codes
- The 4 qubit code and recovery presented by Leung et.al. matches the 5 qubit QEC
- Known as approximate error correction as the recovery permits small distortion

- By channel-adapting the recovery operations, the 4 and 5 qubit codes continue their equivalence

Outline fidelity in both examples

- Numerical Tools for Channel-Adaptation
- Optimum Channel-adapted Quantum Error Recovery (QER)
- Structured Near-optimal Channel-adapted QER

- Channel-Adaptation for the Amplitude Damping Channel
- Conclusions and Open Questions

Motivation for Near-optimal fidelity in both examplesChannel-adapted QER

Three drawbacks of optimal QER

- SDP for n-length code requires 4n+1 optimization variables
- Difficult to compute for codes beyond 5 qubits

- Optimal recovery may be difficult to implement
- Constrained to be valid quantum operation, but circuit complexity is not considered

- Optimal recovery operation provides little insight into channel-adapted mechanism
- Numerical result is hard to analyze for intuition
Optimum QER demonstrates inefficiency of generic QEC and provides a performance bound, but is only a first step toward channel-adapted error correction.

- Numerical result is hard to analyze for intuition

Projective Syndrome Measurement fidelity in both examples

Determine Projective

Measurement

Operator P.

Given Outcome P

Determine

Correction Term.

- Recovery operations can always be interpreted as a syndrome measurement followed by a syndrome correction
- Projective measurements are intuitively and physically simpler to understand than the general measurement
- Examination of optimal recovery examples suggest that projective syndromes approximate optimality

- By selecting a projective measurement, we partition the recovery problem into a set of smaller problems
- Challenge is to select a near-optimal projective measurement

Connection to Eigen-analysis fidelity in both examples

- Consider constraining recoveries to projective measurements followed by unitary operations
- Done in CSS codes, stabilizer codes
- One consequence:

- Write the entanglement fidelity problem in terms of the eigenvectors (Kraus operators) of the Choi matrix
- If were the only constraint, the solution would be the eigen-decomposition of
- CPTP constraint is not the same, but they are similar

- From this observation, we construct a near-optimal algorithm dubbed ‘EigQER’

EigQER Algorithm fidelity in both examples

- Initialize .
For the kth iteration:

- Determine the eigenvector associated with the largest eigenvalue of .
- Determine recovery operator as the closest isometry to using the singular value decomposition.
- Update by projecting out the space spanned by :
Iterate until the recovery operation is complete:

EigQER Example fidelity in both examples5 Qubit Amplitude Damping Channel

- For small g, EigQER and Optimal QER are nearly indistinguishable
- Performances diverge somewhat as noise level increases

- Asymptotic behavior approaching g=0 are identical

EigQER Example fidelity in both examplesAmplitude Damping for Long Codes

- QEC performance worse for longer codes
- Generic recovery only corrects single qubit errors

- Strong performance of Channel-adapted Shor code (9 qubits)
- 8 redundant qubits aids adaptability

- Steane code (7 qubits) performance surprising
- Not well adapted to amplitude damping errors

From optimization theory: Every problem has an associated dual

Dual feasible point is an upper bound for performance

If Dual=Primal then we know it is optimal

Numerical algorithm: construct a dual feasible point given a projective recovery

Near-optimality Claim:Lagrange Dual Upper BoundOutline dual

- Numerical Tools for Channel-Adaptation
- Optimum Channel-adapted Quantum Error Recovery (QER)
- Structured Near-optimal Channel-adapted QER

- Channel-Adaptation for the Amplitude Damping Channel
- Conclusions and Open Questions

The 5 qubit code has 2 dual4=16 syndrome measurements

QEC uses

10 syndromes to correct single X or Y errors

5 syndromes to correct single Z errors

1 syndromes to “correct” Identity (No Error)

Channel-adapted QER uses

1 Syndrome to “correct” Identity Error

5 Syndromes to correct X+iY Errors (approximately)

Remaining 10 syndromes to correct higher order errors

(i.e.Z errors and 2 qubit dampings)

Channel-adaptation more efficiently utilizes the redundancy of the error correcting code

Degrees of freedom targeted to expected errors

Amplitude Damping Error Syndromes|a|2

E=aI+bX+gY

|g|2

|b|2

Code Subspace

g/4

YError

g/4

New Syndrome

Subspace

XError

[4,1] Code – A Second Look dual

Amplitude damping error on an arbitrary encoded state:

These are clearly orthogonal subspaces correctable errors

Some subspaces only reached by multiple damped qubits; each correspond to |0Li:

Optimal recovery has three components:

- Standard `perfect’ recovery from damping errors
- Partial correction for some multi-qubit damping errors
- Approximate correction of `no damping’ case (optional for small g)

Amplitude Damping Errors in the Stabilizer Formalism dual

- Amplitude damping errors have the form
- How does this act on a state stabilized by g?

- Three cases of interest
- g has an I on the ith qubit
- g has a Z on the ith qubit
- g has an X (or Y) on the ith qubit

- We also know that Ziis a generator
- We can thus determine stabilizers for the damped subspaces.

[4,1] Stabilizer Illustration dual

Code Subspace:

Damped Subspaces:

- We can clearly see each damped subspace is orthogonal to the code subspace
- Mutual orthogonality easier to see by rewriting the generators of the 2nd and 4th
- While not shown, stabilizer analysis allows easy understanding of multiple dampings

[2(M+1),M] Amplitude Damping Codes dual

- [4,1] code generalizes directly to higher rate codes
- Paired qubit structure makes guarantees orthogonality of damped subspaces
- Perfectly corrects first order damping errors
- Partially corrects multiple qubit dampings
- Straightforward quantum circuit implementation for encoding and recovery

[6,2] vs. [4,1] dual2

[7,3] Hamming Amplitude Damping Code dual

- First 3 generators are the classical Hamming code
- [7,4] code that corrects a single bit error

- Fourth generator distinguishes between X and Y errors
- Dedicate 2 syndrome measurements for every damping error

- Perfectly corrects single qubit dampings
- No corrections for multiple qubit dampings

- All X generator generalizes other classical linear codes
- Must be even-parity, single error correcting

- Amplitude damping “redemption” of the Steane code

[7,3] vs. [8,3] dual

Summary dual

- Optimal QER is a semidefinite program
- Phys. Rev. A 75(1):021338, 2007 (quant-ph/0606035)
- Optimality conditions
- Robustness analysis
- Analytically constructed optimal QER for stabilizer code, Pauli errors, and completely mixed input

- Structured near-optimal QER operations
- Phys. Rev. A (to appear) (quant-ph/0708.3658)
- EigQER, OrderQER, Block SDP QER
- More computationally scaleable, more physically realizable

- Performance upper bounds via Lagrange duality
- Gershgorin upper bound
- Iterated dual bound

- Amplitude damping channel-adapted codes
- Analysis of optimal QER for [4,1] code of Leung et. al.
- [2(M+1),M] stabilizer codes
- Even parity classical linear codes (1-error correcting)
- Both classes have Clifford group recovery operations
- quant-ph/0710.1052

Open Question: Channel-adapted dualFault Tolerant Quantum Computing

- QEC is the foundation for research in fault tolerant quantum computing (FTQC)
- QEC models noisy channel between two perfect quantum computers
- FTQC explores computing with faulty quantum gates

- Channel-adapted theory will have practical value when extended to channel-adapted FTQC
- Must demonstrate universal set of fault tolerant gates
- Must show that errors do not propagate

- Requires determining a physical noise model and designing a channel-adapted scheme
- Principles and tools of QEC will be the launching point for this analysis

Download Presentation

Connecting to Server..